import gym
import torch
import torch.nn.functional as F
# import numpy as np
# import matplotlib.pyplot as plt
import rl_utils


class PolicyNet(torch.nn.Module):
    def __init__(self, state_dim, hidden_dim, action_dim):
        super(PolicyNet, self).__init__()
        self.fc1 = torch.nn.Linear(state_dim, hidden_dim)
        self.fc2 = torch.nn.Linear(hidden_dim, action_dim)

    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = F.softmax(self.fc2(x), dim=1)
        return x
    
    
class ValueNet(torch.nn.Module):
    def __init__(self, state_dim, hidden_dim):
        super(ValueNet, self).__init__()
        self.fc1 = torch.nn.Linear(state_dim, hidden_dim)
        self.fc2 = torch.nn.Linear(hidden_dim, 1)

    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x
    
    
class ActorCritic:
    def __init__(self, state_dim, hidden_dim, action_dim, actor_lr, critic_lr,gamma, device):
        self.actor = PolicyNet(state_dim, hidden_dim, action_dim).to(device)
        self.actor_optimizer = torch.optim.Adam(self.actor.parameters(),lr=actor_lr)
        self.critic = ValueNet(state_dim, hidden_dim).to(device)  # 价值网络
        self.critic_optimizer = torch.optim.Adam(self.critic.parameters(),lr=critic_lr)  # 价值网络优化器
        self.gamma = gamma
        self.device = device

    def take_action(self, state):
        state = torch.tensor([state], dtype=torch.float).to(self.device)
        probs = self.actor(state)
        action_dist = torch.distributions.Categorical(probs)
        action = action_dist.sample()
        return action.item()

    def update(self, transition_dict):
        states = torch.tensor(transition_dict['states'],dtype=torch.float).to(self.device)
        actions = torch.tensor(transition_dict['actions']).view(-1, 1).to(self.device)
        rewards = torch.tensor(transition_dict['rewards'],dtype=torch.float).view(-1, 1).to(self.device)
        next_states = torch.tensor(transition_dict['next_states'],dtype=torch.float).to(self.device)
        dones = torch.tensor(transition_dict['dones'],dtype=torch.float).view(-1, 1).to(self.device)

        # 时序差分目标
        td_target = rewards + self.gamma * self.critic(next_states) * (1 -dones)
        # 时序差分误差
        td_delta = td_target - self.critic(states)  
        
        
        log_probs = torch.log(self.actor(states).gather(1, actions))
        actor_loss = torch.mean(-log_probs * td_delta.detach())
        self.actor_optimizer.zero_grad()
        actor_loss.backward()  # 计算策略网络的梯度
        self.actor_optimizer.step()  # 更新策略网络的参数
        
        critic_loss = torch.mean(F.mse_loss(self.critic(states), td_target.detach())) # 均方误差损失函数
        self.critic_optimizer.zero_grad()
        critic_loss.backward()  # 计算价值网络的梯度
        self.critic_optimizer.step()  # 更新价值网络的参数

